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Ian R. Petersen
Researcher at Australian National University
Publications - 992
Citations - 24919
Ian R. Petersen is an academic researcher from Australian National University. The author has contributed to research in topics: Quantum & Robust control. The author has an hindex of 67, co-authored 959 publications receiving 22649 citations. Previous affiliations of Ian R. Petersen include University of Cambridge & University of Manchester.
Papers
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Journal ArticleDOI
Linear Quantum System Transfer Function Realization Using Static Networks for Input/Output Processing and Feedback
TL;DR: A new method is proposed, where the transfer function of an LQSS is realized by a series connection of two linear static networks, and a reduced LQ SS, built on an SVD-like decomposition for doubled-up matrices in Krein spaces.
Journal ArticleDOI
Static and dynamic coherent robust control for a class of uncertain quantum systems
TL;DR: This paper proposes two methods to design coherent robust controllers for a class of uncertain linear quantum systems subject to quadratic perturbations in the system Hamiltonian, to formulate a static quantum controller by adding a controller Hamiltonian to the given system.
Book ChapterDOI
Advanced Control of Atomic Force Microscope for Faster Image Scanning
TL;DR: In this article, an observer-based model predictive control (OMPC) scheme was proposed to compensate for the effects of creep, hysteresis, cross-coupling, and vibration in piezoactuators in order to improve the nanopositioning of an atomic force microscopy (AFM) in high scanning speed.
Proceedings ArticleDOI
A Gramian-based approach to model reduction for uncertain systems
Li Li,Ian R. Petersen +1 more
TL;DR: The technical note introduces controllability and observability Gramians in terms of certain parameterized algebraic Riccati inequalities and three model reduction approaches are investigated for the underlying uncertain systems.
Proceedings ArticleDOI
Planar Cooperative Extremum Seeking with Guaranteed Convergence Using A Three-Robot Formation
TL;DR: The proposed scheme is proven to exponentially and simultaneously acquire the specified geometric formation and drive the lead robot to a specified neighbourhood disk around the maximizer, whose radius depends on the specified desired formation size as well as the norm bounds of the Hessian of the field function.